Code, data and figures for Fast and Scalable Implementation of the Bayesian SVM

Kloft, Marius

Code, data and figures for Fast and Scalable Implementation of the Bayesian SVM

Posted on 2018-01-04 - 10:40

This data collection contains the Julia code package, real-world test datasets and figures for the Bayesian SVM algorithm described in the ECML PKDD 2017 paper; Wenzel et al.: Bayesian Nonlinear Support Vector Machines for Big Data.

Code files are provided in .jl format; containing Julia language code: a high-performance dynamic programming language for numerical computing. These files can be accessed by openly available text edit software. To run the code please see the description below or the more detailed wiki. Data files are in .data format used by Analysis Studio, a statistical analysis and data mining program. It contains mined data in a plain text, tab-delimited format, including an Analysis Studio file header. The raw data is can be openly accessed via text edit software. Figures are openly-accessible pdf files.

Background

We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.

Please also check out our github repository:

https://github.com/theogf/BayesianSVM.jl

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Wenzel, Florian; Galy-Fajou, Théo; Deutsch, Matthäus; Kloft, Marius (2018). Code, data and figures for Fast and Scalable Implementation of the Bayesian SVM. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.3922483

https://doi.org/10.6084/m9.figshare.c.3922483

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